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We've implemented a Python package \verb'Subgradient-PY' (\verb'SPY') that solves convex optimization problems using subgradient methods. Some features of \verb'SPY' includes the following.
\begin{itemize}
\item The objective function and constraints need not be differentiable.
\item Due to little space usage, \verb'SPY' can be used to solve large problems.
\item \verb'SPY' can be combined with primal or dual decomposition techniques to develop distributed algorithms.
\end{itemize}
